Multi-frame-based Cross-domain Image Denoising for Low-dose Computed Tomography
Y Lu, Z Xu, MH Choi, J Kim, SW Jung - arXiv preprint arXiv:2304.10839, 2023 - arxiv.org
Computed tomography (CT) has been used worldwide for decades as one of the most
important non-invasive tests in assisting diagnosis. However, the ionizing nature of X-ray …
important non-invasive tests in assisting diagnosis. However, the ionizing nature of X-ray …
Cross-domain Denoising for Low-dose Multi-frame Spiral Computed Tomography
Y Lu, Z Xu, MH Choi, J Kim… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Computed tomography (CT) has been used worldwide as a non-invasive test to assist in
diagnosis. However, the ionizing nature of X-ray exposure raises concerns about potential …
diagnosis. However, the ionizing nature of X-ray exposure raises concerns about potential …
Unsupervised learning-based dual-domain method for low-dose CT denoising
J Yu, H Zhang, P Zhang, Y Zhu - Physics in Medicine & Biology, 2023 - iopscience.iop.org
Objective. Low-dose CT (LDCT) is an important research topic in the field of CT imaging
because of its ability to reduce radiation damage in clinical diagnosis. In recent years, deep …
because of its ability to reduce radiation damage in clinical diagnosis. In recent years, deep …
An unsupervised two‐step training framework for low‐dose computed tomography denoising
W Kim, J Lee, JH Choi - Medical Physics, 2024 - Wiley Online Library
Background Although low‐dose computed tomography (CT) imaging has been more widely
adopted in clinical practice to reduce radiation exposure to patients, the reconstructed CT …
adopted in clinical practice to reduce radiation exposure to patients, the reconstructed CT …
Benchmarking Deep Learning-Based Low Dose CT Image Denoising Algorithms
Long lasting efforts have been made to reduce radiation dose and thus the potential
radiation risk to the patient for computed tomography acquisitions without severe …
radiation risk to the patient for computed tomography acquisitions without severe …
RHLNet: Robust Hybrid Loss-based Network for Low-Dose CT Image Denoising
Low-dose computed tomography (LDCT) is a viable solution for clinical diagnosis despite
noise and artifacts degrading diagnostic quality. Additionally, patients are protected from …
noise and artifacts degrading diagnostic quality. Additionally, patients are protected from …
Unpaired low‐dose computed tomography image denoising using a progressive cyclical convolutional neural network
Q Li, R Li, S Li, T Wang, Y Cheng, S Zhang… - Medical …, 2024 - Wiley Online Library
Background Reducing the radiation dose from computed tomography (CT) can significantly
reduce the radiation risk to patients. However, low‐dose CT (LDCT) suffers from severe and …
reduce the radiation risk to patients. However, low‐dose CT (LDCT) suffers from severe and …
A cascaded convolutional neural network for x-ray low-dose CT image denoising
Image denoising techniques are essential to reducing noise levels and enhancing diagnosis
reliability in low-dose computed tomography (CT). Machine learning based denoising …
reliability in low-dose computed tomography (CT). Machine learning based denoising …
Sharpness-aware low-dose CT denoising using conditional generative adversarial network
Low-dose computed tomography (LDCT) has offered tremendous benefits in radiation-
restricted applications, but the quantum noise as resulted by the insufficient number of …
restricted applications, but the quantum noise as resulted by the insufficient number of …
Learning low‐dose CT degradation from unpaired data with flow‐based model
Background There has been growing interest in low‐dose computed tomography (LDCT) for
reducing the X‐ray radiation to patients. However, LDCT always suffers from complex noise …
reducing the X‐ray radiation to patients. However, LDCT always suffers from complex noise …